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Biostatistics

Jin Zhou

I was trained in mathematics, statistics, and genetics. My work on statistical genomics and biomedical informatics focuses on developing statistically powerful and computationally efficient tools for biobank scale genetic association studies, metagenomics data analysis, and personalized treatment prediction using electronic medical records (EHRs) data. I work at the interface of statistics, genetics, and biomedical informatics and aim to better utilize “big” health-related data for personalized diabetes care.

Michele Guindani

Dr. Guindani received his Ph.D. degree in Statistics from Università Bocconi (Milan, Italy) in 2005 under the supervision of Sonia Petrone (Department of Decision Sciences, Università Bocconi, Italy) and Alan E. Gelfand (Department of Statistical Sciences, Duke University). From 2005 to 2007, Dr. Guindani was a postdoctoral fellow in the Department of Biostatistics at the University of Texas MD Anderson Cancer Center, working with Peter Mueller and Gary L. Rosner.

Jingyi Jessica Li

Dr. Jingyi Jessica Li is a Professor in the Department of Biostatistics, UCLA Fielding School of Public Health. Her specific appointments are in the Department of Statistics (primary) and the departments of Biostatistics, Computational Medicine, and Human Genetics (secondary) at University of California, Los Angeles (UCLA). She is also a faculty member in the Interdepartmental Ph.D. Program in Bioinformatics.

Warren Scott Comulada

W. Scott Comulada, DrPH, is an associate professor-in-residence in the UCLA Departments of Psychiatry and Biobehavioral Sciences and Health Policy and Management. He is also a Methods Core Co-Director for the Center for HIV Prevention, Identification, and Treatment Services (CHIPTS) and an Analytic Core Project Lead for an Adolescent Trials Network (ATN) U19. His roles on CHIPTS and the ATN have supported the development of electronic data collection systems and evaluations of large-scale intervention trials for HIV prevention and treatment. Dr.

Zhe Fei

My research focuses on developing novel statistical methods for big data, meaning extremely large data sets with complex structures and high dimensional n, p, or both. High dimensional inference refers to the uncertainty measures of the statistical models, including asymptotic convergence, confidence intervals and hypothesis testing, which possesses unique challenges that have been drawing substantial research attention in recent years.

Hilary Aralis

Hilary Aralis, Ph.D., is a professor in the UCLA Fielding School of Public Health Department of Biostatistics. She provides statistical expertise to the Nathanson Family Resilience Center (NFRC) and other Centers within the UCLA Department of Psychiatry Division of Population and Behavioral Health. As a member of the Research and Evaluation Team at NFRC, she contributes to research design and oversees statistical analyses for a wide range of projects and programs designed to promote behavioral health at the population level.

Alexandra M. Binder

My research has focused on the potential of epigenetic epidemiology to elucidate the pathway by which adult disease susceptibility is influenced by environmental stimuli during critical periods of plasticity in fetal development. To explore multiple facets of this regulatory network, I have worked to develop expertise in the preprocessing and analysis of sequencing and microarray approaches to interrogate genetic variation, chromatin modifications, mRNA expression, and DNA methylation. In association with the post-doctoral molecular biologists in Dr.

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